corsika::EMThinning Class Reference

This process implements thinning for EM splitting processes (1 -> 2). More...

#include <EMThinning.hpp>

Inheritance diagram for corsika::EMThinning:

Public Member Functions
	EMThinning (HEPEnergyType threshold, double maxWeight, bool const eraseParticles=true)
	Construct a new EMThinning process. More...
template<typename TStackView >
void	doSecondaries (TStackView &)
	Apply thinning to secondaries. More...

Additional Inherited Members
Public Types inherited from corsika::BaseProcess< EMThinning >
using	process_type = EMThinning
	Base processor type for use in other template classes.
Static Public Attributes inherited from corsika::BaseProcess< EMThinning >
static bool const	is_process_sequence
static bool const	is_switch_process_sequence
Protected Member Functions inherited from corsika::BaseProcess< EMThinning >
EMThinning &	getRef ()
const EMThinning &	getRef () const
Protected Attributes inherited from corsika::BaseProcess< EMThinning >
friend	TDerived

Detailed Description

This process implements thinning for EM splitting processes (1 -> 2).

Definition at line 23 of file EMThinning.hpp.

Constructor & Destructor Documentation

EMThinning()

corsika::EMThinning::EMThinning	(	HEPEnergyType	threshold,
		double	maxWeight,
		bool const	eraseParticles = true
	)

Construct a new EMThinning process.

Parameters

threshold	thinning applied below this energy
maxWeight	maximum allowed weight

Member Function Documentation

doSecondaries()

template<typename TStackView >

void corsika::EMThinning::doSecondaries

(

TStackView &

)

Apply thinning to secondaries.

Only EM primaries with two EM secondaries are considered.

If the maximum weight is still out of reach, Hillas thinning is applied (i.e. one of the two secondaries is kept, the other one discarded). If the acceptance probabilities would lead to a weight factor exceeding the maximum weight, we resort to statistical thinning (i.e. the secondaries are kept/discared randomly each on is own). In that case, acceptance probabilities can be assigned without constraints (sum does not need to be 1) and we increase the acceptance probability such that the maximum weight is not exceeded.

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The documentation for this class was generated from the following file:

• corsika/modules/thinning/EMThinning.hpp